scholarly journals Потоковая обработка многоканальных сигналов электроэнцефалограммы с использованием технологии параллельных вычислений на графических процессорах NVIDIA CUDA

2018 ◽  
Vol 44 (10) ◽  
pp. 103
Author(s):  
В.В. Грубов ◽  
В.О. Недайвозов
Keyword(s):  
Neural Activity ◽  
Brain Computer Interface ◽  
Spectral Characteristics ◽  
Cognitive Activity ◽  
Computer Interface ◽  
Computing Technology ◽  
Eeg Signals ◽  
Nvidia Cuda ◽  
Eeg Data

AbstractProspects of using parallel computing technology (PaCT) methods for the stream processing and online analysis of multichannel EEG data are considered. It is shown that the application of PaCT to calculation and evaluation of spectral characteristics of EEG signals makes online determination of changes in the energy of the main rhythms of neural activity in various parts of the cerebral cortex possible. The possibility of implementing the PaCT algorithm with CUDA C library and its use in a modern brain–computer interface (BCI) for cognitive-activity monitoring in the course of visual perception.

An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Damodar Reddy Edla ◽  
Shubham Dodia ◽  
Annushree Bablani ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Brain Computer Interface ◽  
Wavelet Packet ◽  
Stimulus Onset ◽  
Computer Interface ◽  
Band Pass Filter ◽  
Eeg Signals ◽  
Pass Filter ◽  
Eeg Data ◽  
Identification Test ◽  
Band Pass

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.

scholarly journals Fast DCT algorithms for EEG data compression in embedded systems

2015 ◽  
Vol 12 (1) ◽  
pp. 49-62 ◽  
Author(s):  
Darius Birvinskas ◽  
Vacius Jusas ◽  
Ignas Martisius ◽  
Robertas Damasevicius
Keyword(s):  
Embedded Systems ◽  
Data Compression ◽  
High Speed ◽  
Communication Channel ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface ◽  
Eeg Signals ◽  
Eeg Data ◽  
Transform Coefficients

Electroencephalography (EEG) is widely used in clinical diagnosis, monitoring and Brain - Computer Interface systems. Usually EEG signals are recorded with several electrodes and transmitted through a communication channel for further processing. In order to decrease communication bandwidth and transmission time in portable or low cost devices, data compression is required. In this paper we consider the use of fast Discrete Cosine Transform (DCT) algorithms for lossy EEG data compression. Using this approach, the signal is partitioned into a set of 8 samples and each set is DCT-transformed. The least-significant transform coefficients are removed before transmission and are filled with zeros before an inverse transform. We conclude that this method can be used in real-time embedded systems, where low computational complexity and high speed is required.

scholarly journals Identification of ERD using Fuzzy Inference Systems for Brain-Computer Interface

2011 ◽  
Vol 6 (3) ◽  
pp. 403 ◽  
Author(s):  
Ioan Dzitac ◽  
Tiberiu Vesselényi ◽  
Radu Cătălin Ţarcă
Keyword(s):  
Fuzzy Inference ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Inference System ◽  
Eeg Data ◽  
Recent Developments ◽  
Potential Activity ◽  
Inference Systems

A Brain-Computer Interface uses measurements of scalp electric potential (electroencephalography - EEG) reflecting brain activity, to communicate with external devices. Recent developments in electronics and computer sciences have enabled applications that may help users with disabilities and also to develop new types of Human Machine Interfaces. By producing modifications in their brain potential activity, the users can perform control of different devices. In order to perform actions, this EEG signals must be processed with proper algorithms. Our approach is based on a fuzzy inference system used to produce sharp control states from noisy EEG data.

scholarly journals Identification of ERD using Fuzzy Inference Systems for Brain-Computer Interface

2011 ◽  
Vol 6 (3) ◽  
pp. 403 ◽  
Author(s):  
Ioan Dzitac ◽  
Tiberiu Vesselényi ◽  
Radu Cătălin Ţarcă
Keyword(s):  
Fuzzy Inference ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Inference System ◽  
Eeg Data ◽  
Recent Developments ◽  
Potential Activity ◽  
Inference Systems

A Brain-Computer Interface uses measurements of scalp electric potential (electroencephalography - EEG) reflecting brain activity, to communicate with external devices. Recent developments in electronics and computer sciences have enabled applications that may help users with disabilities and also to develop new types of Human Machine Interfaces. By producing modifications in their brain potential activity, the users can perform control of different devices. In order to perform actions, this EEG signals must be processed with proper algorithms. Our approach is based on a fuzzy inference system used to produce sharp control states from noisy EEG data.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

scholarly journals A dataset of EEG signals from a single-channel SSVEP-based brain computer interface

Data in Brief ◽  
2021 ◽  
Vol 35 ◽  
pp. 106826
Author(s):  
Giovanni Acampora ◽  
Pasquale Trinchese ◽  
Autilia Vitiello
Keyword(s):  
Single Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals

Pilot Study on Using Innovative Counting Peaks Method for Assessment Purposes of the EEG Data Recorded from a Single-Channel Non-Invasive Brain-Computer Interface

2021 ◽  
Author(s):  
Natalia Browarska ◽  
Jaroslaw Zygarlicki ◽  
Mariusz Pelc ◽  
Michal Niemczynowicz ◽  
Malgorzata Zygarlicka ◽  
...  
Keyword(s):  
Pilot Study ◽  
Single Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Non Invasive ◽  
Eeg Data
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